Article in Dr. Dobb’s Journal looking at the CPU/memory tradeoffs and the importance of balanced supercomputing architectures.
Industry Perspectives
Podcast: Deep Learning for Scientific Data Analysis
In this NERSC News Podcast, Debbie Bard from NERSC describes how Deep Learning can help scientists accelerate their research. "Deep learning is enjoying unprecedented success in a variety of commercial applications, but it is also beginning to find its footing in science. Just a decade ago, few practitioners could have predicted that deep learning-powered systems would surpass human-level performance in computer vision and speech recognition tasks." [Read More...]
White Papers
PLASTER: A Framework for Deep Learning Performance
Both hardware and software advances in deep learning (DL), a type of ML, appear to be catalysts for the early stages of a phenomenal AI growth trend. Download the new white paper from NVIDIA that addresses the challenges described in PLASTER, which is important in any deep learning solution, and it is especially useful for developing and delivering the inference engines underpinning AI-based services.
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